code: def __init__(self, cfg):
      self.aug = ResizeShortestEdge(
          [cfg.input.min_size_test, cfg.input.min_size_test],
          cfg.input.max_size_test,
      )
      self.input_format = cfg.input.format
      self.size_divisibility = cfg.size_divisibility
      self.pad_value = cfg.PAD_VALUE
      self.max_image_size = cfg.input.max_size_test
      self.device = cfg.model.device
      self.pixel_std = (
          torch.tensor(cfg.model.pixel_std)
          .to(self.device)
          .view(len(cfg.model.pixel_std), 1, 1)
      )
      self.pixel_mean = (
          torch.tensor(cfg.model.pixel_mean)
          .to(self.device)
          .view(len(cfg.model.pixel_std), 1, 1)
      )
      self.normalizer = lambda x: (x - self.pixel_mean) / self.pixel_std
  
file_path: mmf/tools/scripts/features/frcnn/processing_image.py
imports: from typing import Tuple
  import torch
  import torch.nn.functional
  from mmf.datasets.processors.frcnn_processor import img_tensorize, ResizeShortestEdge
label_desc: Use image node_id 1 to create a new Preprocess object  with example: obj = Preprocess(cfg)